jacke121 / yolov3-in-pytorch Goto Github PK

1. Using Resnet50 as detection backbone instead of Darknet53.
2. Switch the individual binary classifier with softmax cross-entropy loss

1. Full pytorch and numpy implementation of YOLOv3 training and detection
2. Pytorch 0.4 codes
3. Multi-Scale Training mentioned in the paper -> so far as I noticed, this one is not implemented by a lot of posts about yolo out there.
4. lots of data augmentation,thanks to imgaug:
5. training in coco2017 dataset

First, clone this repo by input:

git clone https://github.com/TreB1eN/Yolov3-in-Pytorch.git

1. Secondly, download the trained model from cloud drive into data/model folder

   • Google Drive:

     • Cuda Model download:
     • CPU Model download:

   • Baidu Netdisk:

     • Cuda Model download:
     • CPU Model download:

2. For detection in single image, please run

python detect_on_image.py -f input_image_path -o output_image_path -l detection_level

3. For detection in video, please run

python detect_on_video.py -f input_video_path -o output_video_path -l detection_level

4. For detection in camera, please run

python detect_on_camera.py -l detection_level

1. download train2017.zip, val2017.zip and annotations_trainval2017.zip from coco website to data/coco2017

2.  unzip data/coco2017/train2017.zip
    unzip data/coco2017/val2017.zip
    unzip data/coco2017/annotations_trainval2017.zip
   

3. run

   python train.py
   

   Details and parameters are in the codes

Demo1 Youtube: , Youku: Demo2 Youtube: , Youku:

Todo List:

• Hyperparameters Tuning
• Multi-GPU Support
• trained with original Darknet53
• try Binary Classifier instead of Softmax as discussed in the paper

I dont't have much computing resource, PRs are welcomed !

• original Paper:
• original Implementation:
• website:

This repo is also inspired by marvis/pytorch-yolo2: and qqwweee/keras-yolo3: